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FlaskTrack Laboratory Operations & Data Platform
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Data layer · Data Studio · SQL · Python/R pipelines · data lake

Turn laboratory operations into a governed data platform

Query live operational data, build reusable reports, run sandboxed Python and R transformations, combine instrument outputs, and publish analytical datasets without detaching computation from scientific provenance.

FlaskTrack connects operational Postgres records, protocol-specific data, instrument results, analytical storage, APIs, and custom pipelines through one data layer built on top of the laboratory itself.

Versioned recordsProtocols, scientific entities, files, and controlled releases
Live executionScheduled work, batches, samples, roles, and operational events
Instrument dataLocal ingestion, mapping, provenance, review, and downstream use
Data pipelinesSQL, reports, Python/R transforms, APIs, and analytical storage
Built-in governancePermissions, signatures, audit history, review, and validation support
See Plans & Pricing Talk to FlaskTrack Instrument Data
Operational data architecture

The data layer starts with the operational record

Reporting is not an export bolted onto FlaskTrack. The analytical layer is connected to the workflows, samples, materials, forms, files, instruments, and users that produced the data in the first place.

Operational PostgresQuery the structured laboratory system your team is actively using.
Protocol dataCarry custom form submissions and execution context into analysis.
Instrument resultsUse ingested, mapped, reviewable instrument outputs as analytical inputs.
Sandboxed transformsRun Python and R computation without turning the application server into a notebook host.
Reusable outputsDeliver reports, datasets, exports, APIs, and data-lake artifacts from repeatable logic.
Governed provenanceKeep analytical results connected to organization scope and the source laboratory record.

One analytical layer from lab execution to downstream computation

Reporting is no longer limited to application dashboards. FlaskTrack gives teams a structured path from operational records to queries, reusable datasets, sandboxed transformations, scheduled computation, exported outputs, and external systems.

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System Reports Use built-in reports for samples, batches, protocols, workflows, inventory, procurement, compliance, audits, instruments, and execution history.
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SQL Explorer Query structured laboratory data directly, inspect schemas, join operational tables, preview results, and save reusable report definitions.
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Visual Query Builder Build filters and joins through a guided interface when users need structured reporting without writing every query manually.
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Data Pipelines Compose source, transform, and sink blocks into repeatable analytical workflows that can process laboratory data with Python or R in isolated execution environments.
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Data Lake Query live application data alongside analytical datasets and generated outputs without forcing everything into one operational table model.
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API & External Access Execute saved reports and expose approved analytical outputs to dashboards, services, notebooks, and other downstream systems.
Data pipelines

Build repeatable computation around laboratory data

FlaskTrack Data Pipelines let teams go beyond SQL when analysis requires custom transformations, scientific libraries, data reshaping, file processing, or generated datasets.

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Source Blocks Pull controlled inputs from FlaskTrack data sources, files, report outputs, analytical datasets, or other pipeline blocks.
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Python Transforms Run custom Python code in an isolated worker environment for dataframe operations, scientific analysis, normalization, aggregation, and domain-specific processing.
R
R Transforms Use R for statistical workflows and analysis where existing laboratory or research code is already written around the R ecosystem.
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Typed Inputs & Outputs Connect blocks through defined input and output ports so data movement is explicit, inspectable, and easier to reason about than loosely coupled scripts.
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Sandboxed Execution Execute user-authored analysis in isolated containerized workers rather than running arbitrary computation inside the FlaskTrack web server.
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Reusable Outputs Write processed results back as datasets or report-ready outputs so downstream blocks, reports, and users can work from the same derived data.

From operational tables to a computational workflow

A pipeline can begin with live laboratory records, reshape them through multiple transforms, and produce a dataset designed for reporting, review, modeling, or downstream export.

Source Operational data Samples · batches · instruments · custom forms
Transform Python / R Clean · normalize · aggregate · analyze
Transform Additional blocks Join · calculate · reshape · enrich
Sink Analytical output Dataset · report input · export · downstream API

Custom protocol data becomes analytical data by design

Protocol steps can define the measurements, observations, quality checks, and process variables your lab actually needs. Those submissions remain connected to execution context and can be queried, joined, or processed through pipelines.

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Structured Step Forms Define measurements, observations, QC fields, decisions, and process-specific values directly in protocol execution.
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Queryable Custom Data Preserve lab-specific form submissions as structured analytical records instead of burying them in free-text notes or external spreadsheets.
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Join to Operational Context Query custom values alongside samples, batches, protocols, workflow steps, users, catalog items, instruments, and other operational records.
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Process with Pipelines Feed those values into Python or R transforms when reporting requires calculations, normalization, statistical processing, or dataset preparation.
Unified data layer

Live operations and analytical storage in one query surface

FlaskTrack separates transactional application behavior from scalable analytical work without forcing users to manually rebuild the relationships between them.

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Live Postgres Tables Query current records for workflows, users, samples, batches, inventory, compliance, audits, instruments, and execution history.
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Analytical Storage Keep generated datasets, report outputs, custom analytical records, and large derived artifacts in storage designed for analytical use and retention.
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Apache Arrow Flight Move columnar data efficiently between FlaskTrack's data layer and isolated pipeline workers without coupling computation to the web application process.
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Unified Query Layer Join operational tables with custom analytical datasets so reports can reflect both system records and lab-specific computed data.

AI can help draft the query and debug the transform

FlaskTrack's AI-assisted tools can use the available schema and execution context to help users draft SQL and investigate failed pipeline scripts. The user remains responsible for reviewing and applying the recommendation before publishing or rerunning analytical work.

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AI-assisted SQL Describe the report you need in plain language, receive a draft query grounded in the reporting schema, and review the generated SQL before using it.
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Pipeline Error Guidance Use execution errors and script context to generate a recommended next step when a Python or R transform fails.
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Review Before Execution Keep AI suggestions inspectable. Generated SQL and script recommendations are working material for the user, not invisible changes to analytical logic.

Instrument results can flow directly into the analytical layer

Instrument Connectivity adds another controlled source of laboratory data. Raw instrument artifacts can be ingested, parsed, mapped to FlaskTrack records, reviewed, and then used in reports or downstream analytical workflows.

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Instrument Ingestion Bring files and supported local instrument APIs into FlaskTrack through lightweight agents running inside the laboratory network.
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Parsed Runs & Observations Convert instrument-specific outputs into structured runs, positions, values, units, identifiers, and observations.
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Operational Mapping Connect positions and source identifiers back to Samples, Batches, Inventory Lots, execution records, and Molecular Studio data.
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Downstream Analysis Use reviewed instrument-derived data in reports and analytical pipelines instead of manually copying values into a separate analysis environment.

Reports remain reusable delivery surfaces

Pipelines expand what FlaskTrack can compute, while saved reports remain a practical way to present, share, export, and retrieve approved analytical results.

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Save & Edit Reports Maintain reusable report definitions for operational review, recurring analysis, compliance, procurement, and laboratory management.
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Share Reports Publish report definitions for authorized users without creating a separate spreadsheet copy every time a team needs the same answer.
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Export Results Export report outputs as CSV, JSON, HTML, or Parquet for review, archiving, notebooks, and downstream systems.
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Report API Access Run saved reports programmatically and retrieve structured results for dashboards, services, and external automation.

Useful across the entire laboratory operation

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Workflow & Execution Analytics Analyze protocol usage, step completion, batch progress, sample events, state transitions, outcomes, files, and captured measurements.
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Instrument Analytics Work from structured instrument runs and reviewed observations rather than collecting exports manually from instrument workstations.
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Compliance Reports Track approvals, regulatory surfaces, evidence, restricted actions, exceptions, incidents, and review history.
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Audit Reports Query actor attribution, request context, affected records, timestamps, before-and-after snapshots, and integrity history.

A practical analytical foundation for growing biological teams

  • ✔ Built-in system reports backed by structured operational data
  • ✔ SQL editor, visual query builder, schema preview, result grid, and saved reports
  • ✔ Custom protocol forms that remain queryable alongside execution context
  • ✔ User-defined Python and R transforms executed in isolated pipeline workers
  • ✔ Source, transform, and sink blocks connected through explicit pipeline ports
  • ✔ Live operational tables and analytical datasets available through a unified data layer
  • ✔ Apache Arrow Flight-backed transfer for analytical pipeline workloads
  • ✔ Instrument results that can enter reporting and analytical workflows after ingestion and mapping
  • ✔ AI-assisted SQL drafting and execution-error guidance for analytical work
  • ✔ Saved reports, exports, Parquet outputs, and API access for downstream systems
Buyer evaluation

Start with the analytical decisions your lab needs to make

The strongest reporting and pipeline systems begin with clear questions, reliable source data, controlled computation, and an owner for the resulting metric or dataset.

Operational questions

Identify the throughput, quality, schedule, inventory, instrument, cost, and compliance questions your team needs answered repeatedly.

Source and transformation quality

Validate the workflow fields, instrument mappings, custom forms, identifiers, and pipeline transformations before treating a derived dataset as authoritative.

Access and review

Decide who can write SQL, edit pipeline scripts, publish reports, export data, and approve metrics used for management or regulated review.

From laboratory execution to analytical infrastructure

FlaskTrack turns daily lab activity into a data platform that can support both routine operational reporting and deeper computation. Query live records, process data through isolated pipelines, preserve reusable outputs, and make those results available to the people and systems that need them.

See Plans & Pricing Discuss Your Data Workflows Instrument Connectivity
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